Processing of Seismic-While-Drilling Data from the DrillCAM System Acquired with Wireless Geophones, Top-Drive, and Downhole Vibrations Sensors

We present processing details of seismic-while-drilling data recently acquired on one of the onshore wells by a prototype DrillCAM system with wireless geophones, top-drive, and downhole vibration sensors. The general flow follows an established practice and consists of correlation with a drillbit pilot signal, vertical stacking, and pilot deconvolution. This work's novelty is the usage of the memory-based near-bit sensor with a significant time drift reaching 30-40 minutes at the end of each drilling run. A data-driven automatic time alignment procedure is developed to accurately eliminate time drift error by utilizing the top-drive acceleration sensor as a reference. After the alignment, the processing flow can utilize the top-drive or the near-bit pilots similarly. We show each processing step's effect on the final data quality and discuss some implementation details.

Magnetoelastic Ribbons as Vibration Sensors for Real-Time Health Monitoring of Rotating Metal Beams

In the current work, magnetoelastic material ribbons are used as vibration sensors to monitor, in real time and non-destructively, the mechanical health state of rotating beam blades. The magnetoelastic material has the form of a thin ribbon and is composed of Metglas alloy 2826 MB. The study was conducted in two stages. In the first stage, an experiment was performed to test the ability of the ribbon to detect and transmit the vibration behavior of four rotating blades, while the second stage was the same as the first but with minor damages introduced to the blades. As far as the first stage is concerned, the results show that the sensor can detect and transmit with great accuracy the vibratory behavior of the rotating blades, through which important information about the mechanical health state of the blade can be extracted. Specifically, the fast Fourier transform (FFT) spectrum of the recorded signal revealed five dominant peaks in the frequency range 0–3 kHz, corresponding to the first five bending modes of the blades. The identification process was accomplished using ANSYS modal analysis, and the comparison results showed deviation values of less than 1% between ANSYS and the experimental values. In the second stage, two types of damages were introduced to the rotating blades, an edge cut and a hole. The damages were scaled in number from one blade to another, with the first blade having only one side cut while the last blade had two side cuts and two holes. The results, as was expected, show a measurable shifting on the frequency values of the bending modes, thus proving the ability of the proposed magnetoelastic sensors to detect and transmit changes of the mechanical state of rotating blades in real time.

Performance evaluation of accelerometers ADXL345 and MPU6050 exposed to random vibrational input

The use of vibration monitors is a well-established practice in industrial maintenance, usually vibration sensors are positioned at specific points on the monitored machinery and data are continuously collected to feed a machine operating health control system. Nevertheless, the technology for obtaining the signal, its treatment and analysis is generally expensive, and the financial return is not evident, which justifies the development of low-cost alternatives technologies. In this work was performed an analysis of the responses of two Micro-Electro-Mechanical accelerometers, models ADXL345 and MPU6050, exposed to a low intensity random signal and standard operating frequency. The objective of the analysis was to verify the capacity of these devices to be used as mechanical vibration sensors for rotating machines. For this purpose, offset shift analyzes of the sensors due to the Earth's gravitational field were performed, as well as vibrational spectrum and rectification errors analysis under multiple conditions. The data pointed to a greater uniformity of the MPU6050 response, while several behavioral anomalies were seen in the ADXL345, when these sensors are exposed to the same mechanical signal. The qualitative and quantitative behavior of MPU6050 rectification error was consistent with reported in the literature. It was noted that the methodology used can profile the behavior of sensors, however, it is not sufficient to safely justify the inaccuracies, requiring that the tests be performed on a statistically representative number of sensors from different manufacturers and batches.

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

Effect of γ-ray irradiation on performance of LiNbO(3)-based piezoelectric vibration sensors

LiNbO3 (LN)-based micro-electro-mechanical systems (MEMS) vibration sensors exhibit giant prospection in extreme environments, where exist a great amount of irradiation. However, to the best of our knowledge, it is still unknown whether the irradiation affects the performance of LN-based piezoelectric MEMS sensors. Based on this consideration, it is necessary to model the irradiation environment to investigate the effect of high dosage irradiation on LN-based vibration sensors. Firstly, the theoretical work is done to study the Compton Effect on the Gamma-ray irradiation with Co-60 source. After irradiation, X-ray diffraction (XRD) characterization was performed to verify the effect of irradiation on the crystalline of LN thin film. Meanwhile, the performances of output voltages on the five MEMS devices under various dosage of irradiation are compared. As a result, a neglected shift of 0.02 degrees was observed from the XRD image only under maximum irradiation dosage of 100 Mrad(Si). Moreover, the output voltages of cantilever-beam vibration sensors decrease by 3.1%. Therefore, it is verified that the γ-ray irradiation has very little influence on the LN-based MEMS vibration sensors, which have great attraction on the materials and sensors under high-dose irradiation.

Smart Seismocardiography: A Machine Learning Approach for Automatic Data Processing

Seismocardiography (SCG) is a non-invasive method that measures local vibrations created by the mechanical cardiovascular exercises on the chest wall. Thereby, mechanical movements of the heart are recorded in real-time from vibration sensors positioned on the chest of the subject, to further compute the heart rate and retrieve the SCG waveform. Although such events have been widely studied, robust signal processing methods remain a challenging task. On the other hand, the use of piezoelectric sensors has been favored in recent years due to its features and low cost. However, robust data processing techniques should be developed to increase their performance and reliability. In this work, we propose an attractive method for SCG data processing based on the K-Means clustering algorithm to automatically label waveform events. Interestingly, the SCG signals are recovered from a custom-made device built around an ultra-low-cost piezoelectric sensor. Once the signals are measured, they are pre-processed by spectral filtering. Afterwards, the signal spectrum is used to compute the heart rate (HR). Thereby, the filtered signal is sequentially segmented, and every frame is processed by a light-weight K-Means algorithm. Finally, we show the performance of the smart seismocardiography by analyzing SCG waveforms at different physiological conditions.

Sensors Applied to Bearing Fault Detection in Three-Phase Induction Motors

Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple rotor to the motor frame. Furthermore, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis are powerful tools to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors such as accelerometers. The effectiveness of these techniques is due to the previously developed models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the bearing fault mathematical modeling for these techniques. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize bearing fault detection, which is presented in this review. Finally, this work is a contribution to pushing the frontiers of the bearing fault diagnosis area.

Transparent and Flexible Vibration Sensor Based on a Wheel-Shaped Hybrid Thin Membrane

With the advent of human–machine interaction and the Internet of Things, wearable and flexible vibration sensors have been developed to detect human voices and surrounding vibrations transmitted to humans. However, previous wearable vibration sensors have limitations in the sensing performance, such as frequency response, linearity of sensitivity, and esthetics. In this study, a transparent and flexible vibration sensor was developed by incorporating organic/inorganic hybrid materials into ultrathin membranes. The sensor exhibited a linear and high sensitivity (20 mV/g) and a flat frequency response (80–3000 Hz), which are attributed to the wheel-shaped capacitive diaphragm structure fabricated by exploiting the high processability and low stiffness of the organic material SU-8 and the high conductivity of the inorganic material ITO. The sensor also has sufficient esthetics as a wearable device because of the high transparency of SU-8 and ITO. In addition, the temperature of the post-annealing process after ITO sputtering was optimized for the high transparency and conductivity. The fabricated sensor showed significant potential for use in transparent healthcare devices to monitor the vibrations transmitted from hand-held vibration tools and in a skin-attachable vocal sensor.